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Rule mining over knowledge graphs via reinforcement learning.
- Source :
-
Knowledge-Based Systems . Apr2022, Vol. 242, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community. Many solutions have been proposed for the rule mining from large-scale KGs, which however are limited in the inefficiency of rule generation and ineffectiveness of rule evaluation. To solve these problems, in this paper we propose a generation-then-evaluation rule mining approach guided by reinforcement learning. Specifically, a two-phased framework is designed. The first phase aims to train a reinforcement learning agent for rule generation from KGs, and the second is to utilize the value function of the agent to guide the step-by-step rule generation. We conduct extensive experiments on several datasets and the results prove that our rule mining solution achieves state-of-the-art performance in terms of efficiency and effectiveness. • We propose a reinforcement learning algorithm for rule generation. • We propose an efficient rule mining algorithm guided by the trained RL agent. • Our method achieves the state-of-the-art performance on serveral benchmark datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 242
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 155727722
- Full Text :
- https://doi.org/10.1016/j.knosys.2022.108371